Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Methods
that
accelerate
the
evaluation
of
molecular
properties
are
essential
for
chemical
discovery.
While
some
degree
ligand
additivity
has
been
established
transition
metal
complexes,
it
is
underutilized
in
asymmetric
such
as
square
pyramidal
coordination
geometries
highly
relevant
to
catalysis.
To
develop
predictive
methods
beyond
simple
additivity,
we
apply
a
many-body
expansion
octahedral
and
complexes
introduce
correction
based
on
adjacent
ligands
(i.e.,
cis
interaction
model).
We
first
test
model
adiabatic
spin-splitting
energies
Fe(II)
predicting
DFT-calculated
values
unseen
binary
within
an
average
error
1.4
kcal/mol.
Uncertainty
analysis
reveals
optimal
basis,
comprising
homoleptic
mer
symmetric
complexes.
next
show
solved
basis)
infers
both
DFT-
CCSD(T)-calculated
catalytic
reaction
1
kcal/mol
average.
The
predicts
low-symmetry
with
outside
range
complex
energies.
observe
trans
interactions
unnecessary
most
monodentate
systems
but
can
be
important
combinations
ligands,
containing
mixture
bidentate
ligands.
Finally,
demonstrate
may
combined
Δ-learning
predict
CCSD(T)
from
exhaustively
calculated
DFT
same
fraction
needed
model,
achieving
around
30%
using
alone.
Journal of Chemical Theory and Computation,
Год журнала:
2023,
Номер
20(1), С. 253 - 265
Опубликована: Дек. 20, 2023
An
accurate,
generalizable,
and
transferable
force
field
plays
a
crucial
role
in
the
molecular
dynamics
simulations
of
organic
polymers
biomolecules.
Conventional
empirical
fields
often
fail
to
capture
precise
intermolecular
interactions
due
their
negligence
important
physics,
such
as
polarization,
charge
penetration,
many-body
dispersion,
etc.
Moreover,
parameterization
these
relies
heavily
on
top-down
fittings,
limiting
transferabilities
new
systems
where
experimental
data
are
unavailable.
To
address
challenges,
we
introduce
general
fully
ab
initio
construction
strategy,
named
PhyNEO.
It
features
hybrid
approach
that
combines
both
physics-driven
data-driven
methods
is
able
generate
bulk
potential
with
chemical
accuracy
using
only
quantum
chemistry
very
small
clusters.
Careful
separations
long-/short-range
nonbonding/bonding
key
success
By
mitigate
limitations
pure
long-range
interactions,
thus
largely
increasing
efficiency
scalability
machine
learning
models.
The
thoroughly
tested
poly(ethylene
oxide)
polyethylene
glycol
systems,
giving
superior
accuracies
microscopic
properties
compared
conventional
fields.
This
work
offers
promising
framework
for
development
advanced
wide
range
systems.
Journal of the American Chemical Society,
Год журнала:
2024,
Номер
146(22), С. 15376 - 15392
Опубликована: Май 21, 2024
Couplings
between
vibrational
motions
are
driven
by
electronic
interactions,
and
these
couplings
carry
special
significance
in
energy
transfer,
multidimensional
spectroscopy
experiments,
simulations
of
spectra.
In
this
investigation,
the
many-body
contributions
to
analyzed
computationally
context
clathrate-like
alkali
metal
cation
hydrates,
including
Cs+(H2O)20,
Rb+(H2O)20,
K+(H2O)20,
using
both
analytic
quantum-chemistry
potential
surfaces.
Although
harmonic
spectra
one-dimensional
anharmonic
depend
strongly
on
mode-pair
were,
perhaps
surprisingly,
found
be
dominated
one-body
effects,
even
cases
low-frequency
modes
that
involved
motion
multiple
water
molecules.
The
origin
effect
was
traced
mainly
geometric
distortion
within
monomers
cancellation
effects
differential
couplings,
also
shown
agnostic
identity
ion.
These
outcomes
provide
new
understanding
suggest
possibility
improved
computational
methods
for
simulation
infrared
Raman
Journal of Chemical Theory and Computation,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 14, 2025
The
MBX
software
provides
an
advanced
platform
for
molecular
dynamics
simulations,
leveraging
state-of-the-art
MB-pol
and
MB-nrg
data-driven
many-body
potential
energy
functions.
Developed
over
the
past
decade,
these
functions
integrate
physics-based
machine-learned
terms
trained
on
electronic
structure
data
calculated
at
"gold
standard"
coupled-cluster
level
of
theory.
Recent
advancements
in
have
focused
optimizing
its
performance,
resulting
release
v1.2.
While
inherently
nature
ensures
high
accuracy,
it
poses
computational
challenges.
v1.2
addresses
challenges
with
significant
performance
improvements,
including
enhanced
parallelism
that
fully
harnesses
power
modern
multicore
CPUs.
These
enable
simulations
nanosecond
time
scales
condensed-phase
systems,
significantly
expanding
scope
high-accuracy,
predictive
complex
systems
powered
by
Chemical Society Reviews,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
This
review
offers
a
comprehensive
overview
of
the
development
machine
learning
potentials
for
molecules,
reactions,
and
materials
over
past
two
decades,
evolving
from
traditional
models
to
state-of-the-art.
The Journal of Chemical Physics,
Год журнала:
2025,
Номер
162(18)
Опубликована: Май 13, 2025
This
Perspective
is
focused
on
permutationally
invariant
polynomials
(PIPs).
Since
their
introduction
in
2004
and
first
use
developing
a
fully
potential
for
the
highly
fluxional
cation
CH5+,
PIPs
have
found
widespread
machine
learned
potentials
(MLPs)
isolated
molecules,
chemical
reactions,
clusters,
condensed
phase,
materials.
More
than
100
been
reported
using
PIPs.
The
popularity
of
MLPs
stems
from
fundamental
property
being
with
respect
to
permutations
like
atoms;
this
energy
surfaces.
achieved
global
descriptors
and,
thus,
without
an
atom-centered
approach
(which
manifestly
invariant).
used
directly
linear
regression
fitting
electronic
energies
gradients
complex
landscapes
reactions
numerous
product
channels.
also
as
inputs
neural
network
Gaussian
process
methods
many-body
(atom-centered,
water
monomer,
etc.)
applications,
notably
gold
standard
water.
Here,
we
focus
progress
usage
since
2018,
when
last
review
was
done
by
our
group.
The Journal of Chemical Physics,
Год журнала:
2022,
Номер
157(21)
Опубликована: Ноя. 16, 2022
An
accurate,
transferrable,
and
computationally
efficient
potential
energy
surface
is
of
paramount
importance
for
all
molecular
mechanics
simulations.
In
this
work,
by
using
water
as
an
example,
we
demonstrate
how
one
can
construct
a
reliable
force
field
combining
the
advantages
both
physically
motivated
data-driven
machine
learning
methods.
Different
from
existing
models
based
on
many-body
expansion,
adopt
separation
scheme
that
completely
distances,
which
more
convenient
generic
systems.
The
geometry
dependence
atomic
charges
dispersion
coefficients
are
also
introduced
to
improve
accuracy
long-range
part
potential.
new
provides
interpretable
decomposition,
it
accurate
than
conventional
motived
potentials.
Most
importantly,
through
study,
show
information
learn
small
clusters
be
extrapolated
into
larger
systems,
thus
providing
general
recipe
intermolecular
development
at
coupled-cluster
singles
doubles
plus
perturbative
triples
level
theory
in
future.
Journal of Chemical Theory and Computation,
Год журнала:
2023,
Номер
19(13), С. 4308 - 4321
Опубликована: Июнь 29, 2023
The
development
of
molecular
models
with
quantum-mechanical
accuracy
for
predictive
simulations
biomolecular
systems
has
been
a
long-standing
goal
in
the
field
computational
biophysics
and
biochemistry.
As
first
step
toward
transferable
force
biomolecules
entirely
derived
from
"first-principles",
we
introduce
data-driven
many-body
energy
(MB-nrg)
potential
function
(PEF)
N-methylacetamide
(NMA),
peptide
bond
capped
by
two
methyl
groups
that
is
commonly
used
as
proxy
protein
backbone.
MB-nrg
PEF
shown
to
accurately
describe
energetics
structural
properties
an
isolated
NMA
molecule,
including
normal
modes
both
cis
trans
isomers
variation
along
isomerization
path,
well
multidimensional
landscape
NMA–H2O
dimer
gas
phase.
Importantly,
show
fully
transferable,
enabling
dynamics
solution
accuracy.
Comparisons
results
obtained
popular
pairwise-additive
classical
polarizable
demonstrate
ability
represent
effects
interactions
at
short
long
distances,
which
key
guaranteeing
full
transferability
phase
liquid
Since
the
experimental
characterization
of
low-pressure
region
phase
diagram
water
in
early
1900s,
scientists
have
been
on
a
quest
to
understand
thermodynamic
stability
ice
polymorphs
molecular
level.
In
this
study,
we
demonstrate
that
combining
MB-pol
data-driven
many-body
potential
for
water,
which
was
rigorously
derived
from
“first
principles”
and
exhibits
chemical
accuracy,
with
advanced
enhanced-sampling
algorithms,
correctly
describe
quantum
nature
motion
equilibria,
enables
computer
simulations
an
unprecedented
level
realism.
Besides
providing
unique
insights
into
how
enthalpic,
entropic,
nuclear
effects
shape
free-energy
landscape
recent
progress
potentials
simulation
algorithms
has
effectively
opened
door
realistic
computational
studies
complex
systems,
thus
bridging
gap
between
experiments
simulations.
Journal of Chemical Theory and Computation,
Год журнала:
2023,
Номер
19(16), С. 5572 - 5585
Опубликована: Авг. 9, 2023
Ab
initio
computer
simulations
of
anharmonic
vibrational
spectra
provide
nuanced
insight
into
the
behavior
molecules
and
complexes.
The
computational
bottleneck
in
such
simulations,
particularly
for
ab
potentials,
is
often
generation
mode-coupling
potentials.
Focusing
specifically
on
two-mode
couplings
this
analysis,
combination
a
local-mode
representation
multilevel
methods
demonstrated
to
be
symbiotic.
In
approach,
low-level
quantum
chemistry
method
employed
predict
pairwise
that
should
included
at
target
level
theory
self-consistent
field
(and
similar)
calculations.
Pairs
are
excluded
by
approach
"recycled"
low
theory.
Furthermore,
because
pre-screening
will
eventually
become
sufficiently
large
chemical
systems,
distance-based
truncation
applied
these
predictions
without
substantive
loss
accuracy.
This
yield
sub-wavenumber
fidelity
with
reference
transitions
when
including
only
small
fraction
target-level
couplings;
overhead
predicting
couplings,
employing
distance-based,
cutoffs,
trivial
added
cost.
combined
assessed
series
test
cases,
ethylene,
hexatriene,
alanine
dipeptide.
Vibrational
(VSCF)
were
obtained
an
RI-MP2/cc-pVTZ
potential
dipeptide,
approximately
5-fold
reduction
Considerable
optimism
increased
accelerations
larger
systems
higher-order
also
justified,
based
investigation.